In a survey of primary care physicians taking care of Medicare beneficiaries, an average physician coordinates care with 229 other physicians working in 117 practices. Normalized, this is equivalent to 99 physicians and 53 practices for every 100 Medicare beneficiaries. The sheer magnitude of the US Healthcare system, providing services to more than 350 million patients, makes this a big data problem. Each patient is at the center of a cluster of providers. We seek to measure interconnectedness among health care providers in a system where data tend to be siloed by payor, and therefore do not reflect many inter-provider connections. We construct a unique dataset of selected geographically-defined markets, ultra-penetrated by a single payor and with it, define the epidemiology of health care teams and team structure. Building on our experience in network modeling with insurance claims data, we adapt tools from social network analysis to characterize the nature of fragmented team care in the US health system. We develop computational methods to allow policy makers to understand the big data problem of the healthcare social network infrastructure of linked providers and patients. Beyond social network analysis of provider relationships and the flow of information and influence through large networks, we develop novel social networking methods for health system data using the patient-centered team (essentially a network of providers around a single patient) as the primary unit. A provider network is built where each node is an individual provider. Links between provider pairs are weighted by the number of patients they share. We introduce the network-derived concept of team stability and develop novel methods to quantify it. These innovative metrics allow elucidation of the baseline epidemiology of teams through the first broad-scale system measurements of team structure in the US Healthcare system. We hypothesize that that the national population of providers constantly assorts and re-assorts into a staggeringly large number of different care teams (perhaps millions). As an initial test of the validity of our team constructs, we conduct a formative study of the association between team characteristics and health care quality metrics.